{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and Simulation in Python\n",
    "\n",
    "Chapter 17\n",
    "\n",
    "Copyright 2017 Allen Downey\n",
    "\n",
    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "# import functions from the modsim.py module\n",
    "from modsim import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>glucose</th>\n",
       "      <th>insulin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>350</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>287</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>251</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>240</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>216</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>211</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>205</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>196</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>192</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>172</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>163</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>142</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>124</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>105</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>92</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>84</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>82</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>81</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>82</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>82</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>85</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>90</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      glucose  insulin\n",
       "time                  \n",
       "0          92       11\n",
       "2         350       26\n",
       "4         287      130\n",
       "6         251       85\n",
       "8         240       51\n",
       "10        216       49\n",
       "12        211       45\n",
       "14        205       41\n",
       "16        196       35\n",
       "19        192       30\n",
       "22        172       30\n",
       "27        163       27\n",
       "32        142       30\n",
       "42        124       22\n",
       "52        105       15\n",
       "62         92       15\n",
       "72         84       11\n",
       "82         77       10\n",
       "92         82        8\n",
       "102        81       11\n",
       "122        82        7\n",
       "142        82        8\n",
       "162        85        8\n",
       "182        90        7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/glucose_insulin.csv', index_col='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the glucose time series looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data.glucose, 'bo', label='glucose')\n",
    "decorate(xlabel='Time (min)',\n",
    "         ylabel='Concentration (mg/dL)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the insulin time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data.insulin, 'go', label='insulin')\n",
    "decorate(xlabel='Time (min)',\n",
    "         ylabel='Concentration ($\\mu$U/mL)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the book, I put them in a single figure, using `subplot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure to file figs/chap17-fig01.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot(2, 1, 1)\n",
    "plot(data.glucose, 'bo', label='glucose')\n",
    "decorate(ylabel='Concentration (mg/dL)')\n",
    "\n",
    "subplot(2, 1, 2)\n",
    "plot(data.insulin, 'go', label='insulin')\n",
    "decorate(xlabel='Time (min)',\n",
    "         ylabel='Concentration ($\\mu$U/mL)')\n",
    "\n",
    "savefig('figs/chap17-fig01.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolation\n",
    "\n",
    "We have measurements of insulin concentration at discrete points in time, but we need to estimate it at intervening points.  We'll use `interpolate`, which takes a `Series` and returns a function:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The return value from `interpolate` is a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function modsim.modsim.interpolate.<locals>.wrapper(x)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I = interpolate(data.insulin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the result, `I`, to estimate the insulin level at any point in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`I` can also take an array of time and return an array of estimates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_0 = get_first_label(data)\n",
    "t_end = get_last_label(data)\n",
    "ts = linrange(t_0, t_end, endpoint=True)\n",
    "I(ts)\n",
    "type(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the interpolated values look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure to file figs/chap17-fig02.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data.insulin, 'go', label='insulin data')\n",
    "plot(ts, I(ts), color='green', label='interpolated')\n",
    "\n",
    "decorate(xlabel='Time (min)',\n",
    "         ylabel='Concentration ($\\mu$U/mL)')\n",
    "\n",
    "savefig('figs/chap17-fig02.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** [Read the documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) of `scipy.interpolate.interp1d`.  Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "I = interpolate(data.insulin, kind='cubic')\n",
    "plot(data.insulin, 'go', label='insulin data')\n",
    "plot(ts, I(ts), color='green', label='interpolated')\n",
    "\n",
    "decorate(xlabel='Time (min)',\n",
    "         ylabel='Concentration ($\\mu$U/mL)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in `ts`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "G = interpolate(data.glucose)\n",
    "plot(data.glucose, 'bo', label='gluciose data')\n",
    "plot(ts, G(ts), color='blue', label='interpolated')\n",
    "\n",
    "decorate(xlabel='Time (min)',\n",
    "         ylabel='Concentration (mg/dL)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Under the hood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def interpolate(series, **options):\n",
      "    \"\"\"Creates an interpolation function.\n",
      "\n",
      "    series: Series object\n",
      "    options: any legal options to scipy.interpolate.interp1d\n",
      "\n",
      "    returns: function that maps from the index of the series to values\n",
      "    \"\"\"\n",
      "    if has_nan(series.index):\n",
      "        msg = \"\"\"The Series you passed to interpolate contains\n",
      "                 NaN values in the index, which would result in\n",
      "                 undefined behavior.  So I'm putting a stop to that.\"\"\"\n",
      "        raise ValueError(msg)\n",
      "\n",
      "    if not is_strictly_increasing(series.index):\n",
      "        msg = \"\"\"The Series you passed to interpolate has an index\n",
      "                 that is not strictly increasing, which would result in\n",
      "                 undefined behavior.  So I'm putting a stop to that.\"\"\"\n",
      "        raise ValueError(msg)\n",
      "\n",
      "    # make the interpolate function extrapolate past the ends of\n",
      "    # the range, unless `options` already specifies a value for `fill_value`\n",
      "    underride(options, fill_value='extrapolate')\n",
      "\n",
      "    # call interp1d, which returns a new function object\n",
      "    x = magnitudes(series.index)\n",
      "    y = magnitudes(series.values)\n",
      "    interp_func = interp1d(x, y, **options)\n",
      "    units = get_units(series.values[0])\n",
      "\n",
      "    def wrapper(x):\n",
      "        return interp_func(magnitudes(x)) * units\n",
      "    return wrapper\n",
      "\n"
     ]
    }
   ],
   "source": [
    "source_code(interpolate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
